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Ron WorkmanView profile

Research Professor

• Nuclear Physics
• Particle Physics
• Resonance Spectroscopy
• +3 more

Ron Workman is a Research Professor of Physics at George Washington University's Department of Physics, where he teaches undergraduate courses including Classical Mechanics (Physics 3161), Principles of Quantum Physics (Physics 3167), and Stars, Planets, and Life in the Universe (Astronomy 1001). His research focuses on nuclear theory with emphasis on the baryon resonance spectrum through analysis of photon-induced reactions on nucleons using global accelerator data. Education background: PhD in Physics from University of British Columbia (1988) MSc in Physics from University of British Columbia (1984) BSc with Honors in Physics from University of Victoria (1981) Workman's research centers on nuclear theory and particle physics , specializing in partial-wave analysis of pion-nucleon and photon-nucleon scattering data. He develops advanced techniques to extract resonance parameters and pole positions in the complex energy plane, with particular focus on baryon transition form factors and the role of inelastic channels in photoproduction processes. His work bridges experimental data from facilities like Jefferson Laboratory with theoretical frameworks for hadron spectroscopy. Analysis of his recent publications (2022-2024) reveals consistent advancement of partial-wave analysis methodologies including the SAID program and Laurent+Pietarinen expansion techniques. These studies predominantly address photoproduction helicity asymmetries, baryon transition form factors, and exotic state investigations, significantly contributing to the global understanding of nucleon resonance spectra and hadronic interactions. Workman actively contributes to the SAID Data Analysis Center for electromagnetic and hadronic interactions and serves as a key member of the Particle Data Group, authoring multiple editions of the biennial Review of Particle Physics. His collaborative work with the CLAS Collaboration at Jefferson Laboratory represents a major component of his experimental engagement. No information regarding student advising or grant funding is available in the provided materials.

• Machine Learning
• Explainable AI
• Healthcare Data Analytics
• +6 more

Dr. Alison O'Connor is an Assistant Professor at the University of Limerick , affiliated with the Department of Computer Science & Information Systems , Ageing Research Centre , and Lero – the Irish Software Research Centre . Her research bridges machine learning with mechanics of materials and healthcare data analytics , focusing on applications in structural integrity , Industry 4.0 , and medical informatics . Education : PhD in Mechanical Engineering (Imperial College London, 2015-2019), Graduate Diploma in Advanced Materials (2008-2009), and BSc in Aeronautical Engineering (2004-2008). Her research interests span explainable AI for healthcare decision-making, finite element analysis in nuclear steel integrity, and cold rolling process modeling . Recent publications highlight her work on agent-based patient pathway simulations and MRI texture analysis of traumatic brain injury. She contributes to journal peer-review and university committees as a core member. Publication trends show dual expertise: 50% in structural integrity and materials science, 50% in neuroscience and medical diagnostics . Key subfields include dopamine regulation , microdialysis techniques , and computational medicine .

Abdourrahmane ATTOView profile

Professor

• Deep Learning
• Multi-Fractality
• Stochastic Modeling
• +4 more

Abdourrahmane ATTO is a Professor at Polytech Annecy-Chambéry, part of Savoie Mont-Blanc University. His research focuses on advanced machine learning techniques, including deep learning theory, stochastic modeling of multi-fractal processes, and time series analysis of images. He specializes in applications such as SAR image processing, environmental monitoring, and geohazard prediction. His work integrates neural networks, wavelet analysis, and explainable AI methods. Research Themes: Deep Learning Theories (Analysis, Explainability) Multi-Fractality and Stochastic Modeling Time Series of Images & Video Analysis Convolutional Neural Networks SAR and InSAR Image Processing Key Contributions: Developed timed-image representations for action recognition in video sequences. Advanced fractional Brownian field models for texture synthesis and analysis. Created the ISSLIDE dataset for landslide detection using machine learning. Pioneered explainable AI methods for hydrological forecasting and SAR image classification. Labs & Affiliations: Active member of LISTIC laboratory, focusing on interdisciplinary research in signal processing and computer science.

• Ophthalmology
• Corneal Physiology
• Dry Eye Disease
• +3 more

Luisa Holguin Colorado is a researcher at Queensland University of Technology (QUT), holding a PhD in optometry-related research (2016). Her work focuses on corneal physiology, immune cell dynamics, and ocular surface health, with particular emphasis on contact lens effects, diabetic eye complications, and dry eye disease mechanisms. She has published extensively in peer-reviewed journals since 2016, collaborating with institutions globally. Key research areas include: Corneal nerve morphology and its association with diabetes and neuropathic pain Immune cell behavior in the human cornea (dendritic cells, goblet cells) Impact of contact lenses on ocular surface health Novel imaging techniques for eyelid margin analysis Menstrual cycle effects on ocular surface parameters Her 2025 research demonstrates advancements in toric contact lens performance and dry eye-pain interconnections. Collaborations span ophthalmology, immunology, and clinical trials for ophthalmic therapies. She has contributed to 21+ peer-reviewed articles and one doctoral thesis, showcasing interdisciplinary contributions to optometry and vision science.

Matthias VolkView profile

Assistant Professor

• Formal Methods
• Probabilistic Model Checking
• Safety-Critical Systems
• +3 more

Matthias Volk is an Assistant Professor in the Formal System Analysis group at Eindhoven University of Technology (TU/e). His research focuses on improving the safety and reliability of complex systems through formal methods, particularly probabilistic model checking. He develops automated techniques for system modeling and analysis, with a focus on industrial applications. He is a core developer of the Storm model checker. Academic Background: B.Sc. and M.Sc. in Computer Science from RWTH Aachen University Ph.D. from RWTH Aachen University, supervised by Joost-Pieter Katoen Postdoctoral researcher at the University of Twente Joined TU/e as an Assistant Professor in 2023 Research Interests: Probabilistic model checking Safety-critical systems Automated verification tools Industrial collaborations for formal methods Grants and Funding: NWO grant NWA.1160.18.238 (PrimaVera) ERC Consolidator Grant 864075 (CAESAR) EU Horizon 2020 project MISSION (101008233) Labs/Teams: Part of the Formal System Analysis group and Algorithms and Logics for Verification group at TU/e.

• Additive manufacturing for metallic materials
• Advanced materials processing
• Metallic materials
• +9 more

Giovanni Maizza is a Full Professor at the Department of Applied Science and Technology (DISAT) at Politecnico di Torino, Italy. He leads research in advanced materials processing, multiphysics modeling, and additive manufacturing, with applications in aerospace, automotive, and industrial tooling. He has held leadership roles in EU and Italy-Japan collaborative projects and serves on international standards committees. Full Professor, DISAT, Politecnico di Torino Scientific Director of multiple national and commercial research projects Coordinator, Italy-Japan Network of Excellence on Nanomaterials Member, ISO/TC 164 and UNIMET standards committees Guest Editor, MATERIALS (2020–2022) Research Interests: Giovanni Maizza specializes in the development of multiphysics and multiscale methodologies for designing and optimizing materials and manufacturing processes. His work emphasizes reducing experimental burden through advanced simulation and modeling. Key areas include additive manufacturing (especially EBM of Ti6Al4V), spark plasma sintering, laser-based deposition and repair, powder metallurgy, and the processing of nanostructured and superhard materials. His research is highly interdisciplinary, combining computational modeling with experimental validation. Publication Trends: His recent publications focus on spark plasma sintering of ceramics and composites, microstructure modeling in aluminum alloys, and the development of transparent or superhard materials. There is a strong emphasis on process-structure-property relationships, with frequent use of finite element and particle-based simulations to understand thermal, mechanical, and microstructural evolution during processing. Scientific Awards and Recognitions: Fellow, UNIMET (2021–present) Fellow, ISO/TC 164 – Mechanical Testing of Metals (2021–present) Permanent Member, UNI/CT 026/GL 03 – Mechanical Testing of Metallic Materials Guest Editor, MATERIALS journal (2020–2022) Advising and Grants: While specific student names are not listed, Maizza has supervised numerous research projects funded by industry and public grants. He has served as Scientific Director for projects funded by Danieli SpA, KEMEX Ltd, AVIO GE SPA, and Punch Torino SPA. His work includes EU-funded initiatives and collaborations with Japanese laboratories (NIMS, AIST). He has developed custom simulation codes and utilized commercial software for industrial clients. Labs and Research Teams: He is affiliated with the Center of Modelling and Design of Materials and Processes (DISAT), where he leads research integrating computational modeling, experimental testing, and industrial application. His team works on developing self-consistent multiphysics codes using Fortran, MATLAB, and C++, and applies tools like ANSYS, COMSOL, and LS-DYNA.

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +7 more

Eneko Agirre is a Professor in the Department of Computer Science and Artificial Intelligence at the Faculty of Informatics, University of the Basque Country. He is a leading researcher in Natural Language Processing with a strong focus on multilingual systems, particularly for the Basque language, and has published extensively in top NLP conferences including ACL, EMNLP, and NAACL. His research spans multiple areas of computational linguistics including word sense disambiguation, machine translation, cross-lingual learning, and information extraction. Agirre has pioneered work on zero-shot learning approaches, data contamination issues in LLM evaluation, and low-resource language processing. His recent work includes developing the Latxa family of Basque language models and creating novel methods like WiCkeD for more challenging benchmarks and GUIDEX for zero-shot information extraction. Analysis of his recent publications reveals a strong trend toward addressing fundamental challenges in large language model adaptation, evaluation reliability, and cross-lingual transfer learning. His work often combines theoretical insights with practical applications, particularly for under-resourced languages like Basque. His research demonstrates how linguistic typology impacts cross-lingual performance and how to overcome data limitations through innovative methodology. Agirre has mentored numerous researchers who have become active contributors to the NLP field, including Oscar Sainz, Jon Ander Campos, and Iker García-Ferrero. His collaborative work spans institutions across Spain and internationally, reflecting his standing in the global NLP community.

• Process Systems Engineering
• Optimization
• Model Predictive Control
• +5 more

Nikolaos Diaggelakis serves as an Assistant Professor in the Department of Process Development, Analysis & Design (II) at the School of Chemical Engineering, Technical University of Crete, where his work bridges theoretical optimization and industrial process applications. Education: Ph.D. in Chemical Engineering, Imperial College London, 2017 M.Sc. in Chemical Engineering, Imperial College London, 2012 Diploma in Chemical Engineering, National Technical University of Athens, 2011 Visiting Doctoral Researcher, Texas A&M Energy Institute, 2017 His research pioneers process systems engineering through mathematical optimization with moving time horizons, simultaneous design-control integration, and multi-parametric programming frameworks. Key applications span chemical process intensification, energy-efficient microgrids, and environmental systems, emphasizing worst-case and nonlinear optimization robustness. His theoretical contributions directly enable industrial implementations in energy systems and manufacturing. Publications from 2015-2022 reveal a concentrated evolution toward real-time optimization and decentralized control architectures , with increasing focus on data-driven surrogate models and robust explicit MPC strategies. The work consistently bridges fundamental algorithm development (e.g., multi-parametric quadratic programming solutions) with tangible industrial applications like air separation units and combined heat-power systems. Scientific Awards: Excellence Award for Outstanding PhD Thesis in Computer Aided Process Engineering (CAPE), Third Place, 2017 Invited Speaker, Distinguished Young Researchers Seminar Series, Northwestern University, 2016 Best Poster Award, CPSE Autumn Industrial Consortium Meeting, Imperial College London, 2014 Dr. Diaggelakis actively secures competitive funding, including U.S. Department of Energy grants (2018-2020) for smart manufacturing in chemical processing and National Science Foundation projects (2015-2016) on integrated process design-control frameworks. His leadership in the PAROC research group drives software development (POP toolbox) and industry collaborations through consortia like CPSE. Current projects emphasize uncertainty-aware scheduling for process industries and energy-efficient microgrid operation. He co-develops the industry-standard PAROC framework and POP toolbox, enabling multi-parametric optimization for complex process systems, and maintains active partnerships with Texas A&M and Imperial College London through ongoing research consortia.

Adam TorokView profile

Professor

• Sustainable Transport
• Transport Economics
• Energy Engineering
• +7 more

Adam Torok is a Professor and Vice-Dean of the Faculty of Transport Sciences at Budapest University of Technology and Economics (BME), Department of Transport Technology and Economics. He is also affiliated with the KTI – Institute for Transport Sciences in Budapest, where he serves as Professor in Transport Management. His research is centered on sustainable transport systems, energy and emissions modeling, and transport policy in Central Europe. Education: DSc in Transport Sciences, PhD in Business and Management, both from BME. Current Position: Vice-Dean of Faculty, BME; Professor, KTI Institute for Transport Sciences. Laboratory: Laboratory for Sustainable Road Transport, BME. Adam Torok's research interests span sustainable transport, transport economics, energy engineering, environmental impact assessment, urban planning, and climate change mitigation in the transport sector. His work emphasizes the economic and environmental sustainability of public and road transport, with a strong focus on alternative propulsion technologies, autonomous vehicles, and policy analysis. He investigates emissions drivers using decomposition models like Kaya and LMDI, and evaluates the impacts of road pricing, congestion, and mobility transitions. His recent publications reveal a strong trend toward modeling future transport scenarios, particularly concerning energy consumption, emissions, and the adoption of battery electric and autonomous vehicles (AVs) in the EU and Hungary. He frequently employs predictive modeling, scenario analysis, and macroeconomic frameworks. His research integrates economics, engineering, and environmental science to address sustainability challenges in urban and regional mobility. Doctor of Hungarian Academy of Sciences (2021) National Excellence Program Scholarship (2018) Bólyai János Scholarship of Hungarian Academy of Sciences (2016) Bólyai János Scholarship of Hungarian Academy of Sciences (2011) Adam Torok actively collaborates with a wide network of researchers, including Mohammad Maghrour Zefreh, Anas Alatawneh, and Dávid Baranyai. He has contributed to numerous studies on transport performance, public transport accessibility, and the integration of new technologies like AVs and UAVs. He is involved in policy-relevant research, including cost-benefit analysis of transport interventions and the economic implications of public transport support. His work supports evidence-based decision-making in transport planning and sustainability. He leads and contributes to research within the Laboratory for Sustainable Road Transport at BME, focusing on empirical and theoretical analysis of transport systems, emissions, and innovative mobility solutions. His team utilizes advanced modeling techniques, data analysis, and simulation tools to assess the sustainability and efficiency of current and future transport systems.

• Pharmaceutical Technology
• Dissolution Testing
• Drug Delivery Systems
• +3 more

Dr. Katharina Prüßmann serves as an Academic Councillor and Postdoctoral Researcher in the Department of Pharmacy at the Faculty of Chemistry and Pharmacy, Ludwig Maximilian University of Munich (LMU). She leads research initiatives within Prof. Olivia Merkel's group since April 2024, following prior postdoctoral work in Prof. Winter's laboratory at LMU (2021-2024). 2006-2010: Pharmacy School, Eberhard Karls University Tübingen 2010-2011: Pharmacy Residency (Retail Pharmacy, Reutlingen-Betzingen) 2011: Board License as Pharmacist & Hospital Pharmacy Residency (University Hospital Tübingen) 2012-2015: Pharmacist in Retail Pharmacy (Hannover) 2015-2021: PhD in Pharmaceutical Technology, University of Greifswald Her research centers on pharmaceutical technology with emphasis on dissolution testing methodologies, drug delivery system optimization, and in vitro performance assessment. She investigates critical parameters affecting drug release from complex formulations including coated medical devices, contributing to standardized testing protocols that bridge laboratory results with clinical outcomes. Current work in Merkel's group expands into advanced nucleic acid delivery systems. Analysis of her publications reveals consistent focus on dissolution science, particularly vessel geometry impacts and medium composition effects on drug release profiles. Her contributions to USP workshops demonstrate leadership in establishing industry standards for in vitro performance testing, with applications spanning cardiovascular implants to novel drug formulations. Dr. Prüßmann actively contributes to Prof. Olivia Merkel's research ecosystem at LMU, collaborating on ERC-funded projects in drug delivery while mentoring junior researchers. Her work integrates pharmaceutical technology with regulatory science to address real-world drug product challenges.

• Cosmology
• Dark Energy
• Modified Gravity
• +3 more

Tiago Manuel Duarte Marques Barreiro is an Assistant Professor at Universidade Lusófona, specializing in Theoretical Physics and Cosmology. He holds a PhD in Physics from the University of Sussex (1998) and a Licenciatura in Engenharia Física Tecnológica from Universidade de Lisboa (1993). His research focuses on dark energy, modified gravity, and interactions between dark matter and cosmological structures. Education: PhD in Physics (University of Sussex, 1998); Licenciatura in Engenharia Física Tecnológica (Universidade de Lisboa, 1993) Barreiro's work spans theoretical cosmology, including models like f(Q) gravity, coupled quintessence, and dark energy dynamics. He has published 31 journal articles and contributed to major projects like LISA (Laser Interferometer Space Antenna). His research often addresses cosmological tensions (e.g., sigma8) and explores non-canonical fields for dark energy. Recent publications highlight his contributions to gravitational wave cosmology, precision constraints on varying fundamental constants, and forecasting methods for modified gravity theories. His work integrates analytical modeling with observational data from instruments like ESPRESSO and LISA. Scientific Awards: None explicitly mentioned Barreiro has co-supervised 1 PhD thesis and 1 MSc dissertation, contributing to the training of future cosmologists. His affiliations include collaborations with international missions and institutions, reflecting his impact on theoretical cosmology.

Stefan GlunzView profile

Professor

• Photovoltaics
• Solar Cells
• Silicon Technology
• +3 more

Prof. Dr. Stefan Glunz holds the W3 Chair for Photovoltaic Energy Conversion at the Albert Ludwigs University Freiburg and serves as Head of Division Solar Cells at the Fraunhofer-Institute for Solar Energy Systems (ISE). With over 25 years of experience, his research focuses on high-efficiency silicon solar cells, perovskite solar cells, and tandem solar cell structures. He has driven breakthroughs in silicon surface passivation and tunnel oxide contacts (TOPCon). Albert Ludwigs University Freiburg: 2015–present Fraunhofer ISE: 1999–present Research Interests: Silicon-Based Tandem Solar Cells Perovskite Solar Cell Development Semiconductor Defect Physics Advanced Process Simulation Photovoltaics Efficiency Optimization Scientific Awards: 2014: Becquerel Prize 2008: Eni Award 2004: 20% Efficiency Milestone 2015: 25% Efficiency in Dual-Contact Cells His 15 most recent publications (2025–2024) demonstrate expertise in perovskite-silicon tandem engineering, TOPCon technology, and photovoltaic measurement methodologies. Contact: stefan.glunz@inatech.uni-freiburg.de

Carlos Molina HuttView profile

Associate Professor

• Earthquake Engineering
• Structural Engineering
• Seismic Resilience
• +3 more

Dr. Carlos Molina Hutt is an Associate Professor in the Department of Civil Engineering at the University of British Columbia (UBC), specializing in Structural & Earthquake Engineering. He leads the Engineering for Seismic Resilience Laboratory (ESR Lab), focusing on assessing seismic risk in buildings and translating this knowledge into tools for engineers, planners, and policymakers. His work emphasizes societal and economic impacts of earthquake resilience in urban environments. Carlos holds a PhD in Earthquake Engineering from University College London (UCL), an MSc from Stanford University, and a BSc from Villanova University. He is a registered Professional Engineer (PE) in California, a Chartered Engineer (CEng) in the UK, and has extensive industry experience with firms like Arup and consulting for the UN and EU. His research is funded by NSERC, NSF, and international organizations. Research interests include seismic resilience, performance-based design, risk analysis, and high-rise building systems. He teaches courses on structural design and seismic engineering at UBC. Notable awards include the EERI Shah Family Innovation Prize (2019) and UCL’s Global Engagement Award (2015). His collaborations span academia, industry, and cities like San Francisco and Vancouver. Recent work examines seismic risk in sedimentary basins, collapse risk of tall buildings, and post-earthquake recovery modeling. The ESR Lab develops tools to bridge engineering science and policy for resilient urban systems. Key achievements include deploying as a technical expert post-earthquakes in Ecuador (2016) and Mexico (2017), and publishing over 50 peer-reviewed articles. Current projects focus on machine learning for seismic risk assessments and integrating basin amplification factors into hazard models.

• Surface Metrology
• Tool Steel Polishing
• Injection Molding
• +3 more

Sabina Rebeggiani is a Senior Lecturer at the School of Business, Innovation and Sustainability at Halmstad University. Her research focuses on surface metrology, tool steel polishing, and quality assurance in manufacturing processes. She holds a doctoral degree in engineering from Halmstad University (2013) and a licentiate thesis (2009) on polishability of tool steels. Her work emphasizes optimizing surface finishes for industrial applications, particularly in injection molding, automotive components, and sustainable material surfaces. Key contributions include developing methods for in-line surface measurements, defect detection in paint systems, and empirical evaluations of polishing processes. Her research spans over 20 years, with publications in conferences such as ASPE and journals focusing on manufacturing engineering and materials science. She has collaborated on projects exploring robotic-assisted polishing equipment and traceology-based process control. Her recent work (2023-2025) addresses sustainable surface communication in supply chains and objective classification of extruded aluminum surfaces. Dr. Rebeggiani’s contact details include sabina.rebeggiani@hh.se and mobile number 072-977 36 50. She is affiliated with Halmstad University’s innovation-focused research programs and has contributed to industry partnerships in surface characterization and quality assurance.

• Geodesy
• GNSS Meteorology
• Space Geodetic Techniques
• +4 more

Karina Wilgan is a Researcher at the German Research Centre for Geosciences (GFZ) in Potsdam, Germany, working within the Department of Geodesy. Her research spans multiple departments at GFZ, specifically focusing on Space Geodetic Techniques (Department 1.1), Space Physics and Space Weather (Department 1.5), and Hydrology (Department 4.4). Dr. Wilgan's research interests center on the application of Global Navigation Satellite Systems (GNSS) for meteorological purposes, particularly in monitoring tropospheric parameters and water vapor content. Her work bridges geodesy, meteorology, and hydrology, with a strong emphasis on practical applications for weather forecasting and flood monitoring. She has made significant contributions to understanding the severe flooding events that occurred in Germany in July 2021 through advanced multi-GNSS tomography techniques. Her publication record from 2021-2024 demonstrates a consistent research trajectory focused on improving atmospheric monitoring through innovative GNSS applications. The articles collectively show a strong emphasis on operational meteorology, climate research applications, and disaster monitoring, with particular attention to extreme weather events and their atmospheric precursors. Dr. Wilgan collaborates extensively with colleagues at GFZ, particularly with researchers G. Dick, J. Wickert, and F. Zus, forming a core team in GNSS meteorology research. Her work contributes to the Helmholtz Research Program 'Changing Earth – Sustaining our Future,' specifically addressing atmospheric changes in global context.